Multi-objective optimization method for stabilizing output power of vertical shaft type gravity energy storage system

Abstract

The invention discloses a multi-objective optimization method for stabilizing the output power of a vertical shaft type gravity energy storage system, and the method comprises the steps: building a motion model and a power model of the system according to the falling physical process of a weight block, and building an efficiency model and an economic model according to selected system equipment; taking a motion model, an efficiency model and an economic model as constraint conditions, taking minimum power fluctuation ratio and maximum output power as targets to establish a target optimization function, and taking an optimization quantity as a motion parameter of the weight block; solving the optimization model by using a genetic algorithm to obtain motion parameters of the weight block; and determining a v-t curve of the weight block and grid-connected access time of the plurality of subsystems according to the motion parameters, obtaining an output power P-t curve of a single subsystem in the system according to the v-t curve, and sequentially carrying out grid-connected superposition on the plurality of subsystems according to the time to finally output stable power. According to the scheme, the grid-connected requirement of a power grid can be met, and the electric energy quality problem of a vertical shaft type gravity energy storage system is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of gravity energy storage power generation, and in particular relates to a multi-objective optimization method for stabilizing the output power of a vertical shaft gravity energy storage system. Background Art

[0002] Gravity energy storage is a new type of energy storage solution. Some existing research mainly focuses on the optimization of the capacity configuration problem of shaft-type gravity energy storage and wind-solar hybrid systems. It does not involve the optimization of the output power characteristics of the gravity energy storage system, does not involve the optimization of the motion parameters of the heavy block, cannot stabilize the output power curve of the energy storage system, and cannot guarantee the quality of electric energy. It may cause the output power of the system to be relatively small under the premise of small power fluctuation rate, and cannot meet the power demand of the power grid for the energy storage system. This will bring considerable problems to the grid connection of the shaft-type gravity energy storage system and the effect of compensating the grid electricity. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a multi-objective optimization method for stabilizing the output power of a vertical gravity energy storage system, thereby solving the problems of unstable output power, large power fluctuation rate and inability to meet the grid connection requirements of the vertical gravity energy storage system.

[0004] According to the technical solution of the present invention, the present invention provides a multi-objective optimization method for stabilizing the output power of a vertical shaft gravity energy storage system, comprising the following steps: Step S1, establishing a system motion model and a power model according to the physical process of the weight falling, and establishing an efficiency model and an economic model according to the selected system equipment; Step S2, using the motion model, efficiency model and economic model as constraints, and under the condition of meeting the power demand of the power grid, establishing a target optimization function with the goal of minimizing power fluctuation rate and maximizing output power, and the optimization quantity is the motion parameter of the heavy object; Step S3, setting the initial population size N and the number of iterations K, using a genetic algorithm to solve the optimization model, and obtaining the motion parameters of the heavy object; Step S4, determine the vt curve of the weight block and the grid connection time of multiple subsystems according to the motion parameters, and then obtain the output power Pt curve of a single subsystem in the system according to the vt curve. Multiple subsystems are connected to the grid in sequence according to time and superimposed to finally output stable power.

[0005] Furthermore, the motion model and power model are determined by the laws of physics: ; Among them, v is the falling velocity of the weight block, a is the acceleration during the fall, F is the tension of the rope on the weight, g is the acceleration due to gravity, t is time, M is the mass of the weight block, and P is power.

[0006] Furthermore, the efficiency model is used as a constraint of the optimization model. The efficiency of the optimized system should not be lower than the set value. The loss comes from the air resistance and inner wall friction during the operation of the weight, as well as the efficiency of power electronic equipment, mechanical equipment, and motor equipment: ; Where η is the system efficiency, H is the shaft height, and f s is the sliding friction, C w is the air resistance coefficient, A is the windward area, ρ is the air density, T is the atmospheric temperature, η motor is the gearbox efficiency, η inv is the converter efficiency, η G is the motor efficiency.

[0007] Furthermore, the economic model is the levelized cost of electricity, which is an important indicator for measuring the economic benefits of energy storage systems. As a constraint of the optimization model, the levelized cost of electricity of the optimized system should be less than the set value; the composition of the levelized cost of electricity is: ; Among them, LCOE is the levelized cost of electricity, C total is the life cycle cost, life cycle cost C total Including initial investment cost C inv , Annual maintenance and operation cost C OM , replacement cost C R , Power purchase cost C C , Recovery cost C Rec , E life is the life cycle energy output value.

[0008] Furthermore, in step S2, the optimization objectives are power fluctuation rate and average output power: ; Among them, P max is the maximum power, P min is the minimum power value, is the average power; the motion parameters of the weight block include acceleration a, maximum velocity v m , acceleration change rate j.

[0009] Furthermore, in step S3, the genetic algorithm specifically includes: Generate N solutions that meet the restriction conditions according to the set initial population number; Calculate the fitness function of each individual, that is, optimize the target power fluctuation function and the average output power function; Apply the genetic operators of selection, crossover, and mutation to the population to produce a new generation based on the better individuals in the current generation; A new generation is continuously generated according to the set number of iterations K until the set number of iterations is reached or there is no obvious improvement in the individual, then the calculation is stopped.

[0010] 7. The multi-objective optimization method for output power of a stable vertical shaft gravity energy storage system according to claim 1 is characterized in that, in step S4, the power superposition method is specifically to start multiple subsystems at different phases, and the output power of each subsystem has different phases, and a stable output power is obtained by peak shaving filling of power superposition; the grid-connection time of each subsystem is determined by the optimized motion parameters, different motion parameters determine different vt curves, and then determine different Pt curves to obtain the corresponding grid-connection time.

[0011] Furthermore, the grid connection time does not need to be regulated, but is determined by the optimized motion parameters; the superimposed output power changes periodically, and the grid connection time of each subsystem is: t(i) =i*T 下 / n, Where t(i) is the grid connection time of the ith subsystem, n is the total number of subsystems, T 下 is the single drop time of the weight block.

[0012] Compared with the prior art, the beneficial technical effects of the present invention are as follows: The multi-objective optimization method for stabilizing the output power of a vertical gravity energy storage system of the present invention is based on the working principle of the vertical gravity energy storage system, establishes the motion model and power model, efficiency model and economic model of the vertical gravity energy storage system, forms a multi-objective optimization overall model on the basis of coupling these models, and uses a genetic algorithm to optimize the motion parameters of the weight blocks in the system, so that when the output power of multiple vertical shaft channels is superimposed, the vertical gravity energy storage system outputs a stable power curve to the outside; thereby meeting the grid connection requirements of the power grid, outputting a power curve with the smallest fluctuation rate under the premise of a certain power requirement; and outputting a power curve with the largest average power under the premise of a certain fluctuation rate requirement. Solve the power quality problem of the vertical gravity energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a structural diagram of a vertical shaft gravity energy storage system.

[0014] Figure 2 It is a flow chart of a multi-objective optimization method according to an embodiment of the present invention.

[0015] Figure 3 It is a flow chart of a genetic algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The present invention provides a multi-objective optimization method for stabilizing the output power of a vertical gravity energy storage system, so as to solve the problems of unstable output power, large power fluctuation rate and inability to meet the grid connection requirements of the vertical gravity energy storage system. The main purpose of the present invention is to establish a motion model and power model, efficiency model and economic model of the vertical gravity energy storage system based on the working principle of the vertical gravity energy storage system. On the basis of coupling these models, a multi-objective optimization overall model is formed, and a genetic algorithm is used to optimize the motion parameters of the weight blocks in the system, so that when the output power of multiple vertical shaft channels is superimposed, the vertical gravity energy storage system outputs a smooth power curve to the outside. The grid connection requirements of the grid are met, and the power quality problem of the vertical gravity energy storage system is solved.

[0017] See also Figure 1 The vertical shaft gravity energy storage system includes: a mine passage 1, a pulley block 2 for supporting and adjusting the direction, a weight block 3, a rope system 4 connected to the weight block, an electric generator and a power electronic device 5. The working principle of the system is: when storing energy, the weight block is lifted by electric energy, the excess electric energy of the power grid is converted into gravitational potential energy, and electric power is absorbed; when releasing energy, the weight is lowered, the gravitational potential energy is converted into electric energy, and electric power is output.

[0018] The multi-objective optimization method for output power of a stable vertical gravity energy storage system of the present invention is as follows: Figure 2 As shown in the figure, the multi-objective optimization method takes the minimum power fluctuation rate and the maximum output power as the goal, obtains the motion parameters of the weight block through optimization, and obtains the output power curve of the system and the speed curve of the weight block according to the parameters, and then achieves stable power output by superimposing the output powers of multiple subsystems. It specifically includes the following steps.

[0019] Step S1, establishing a motion model and a power model of the system according to the physical process of the falling weight, and establishing an efficiency model and an economic model according to the selected system equipment.

[0020] Step S2, taking the motion model, efficiency model and economic model as constraints, establishing a target optimization function with the goal of minimizing power fluctuation rate and maximizing output power while meeting the power demand of the power grid (specifically, establishing a target optimization function with the goal of minimizing power fluctuation rate while meeting the power demand of the power grid, and establishing a target optimization function with the goal of maximizing output power while meeting the power fluctuation rate demand of the power grid), and the optimization quantity is the motion parameter (or operating parameter) of the heavy object.

[0021] Step S3, setting the initial population size N, the number of iterations K, and using the genetic algorithm to solve the optimization model to obtain the motion parameters of the heavy object.

[0022] Step S4, determine the vt curve of the weight block and the grid connection time of multiple subsystems according to the motion parameters, and then obtain the output power Pt curve of a single subsystem in the system according to the vt curve. Multiple subsystems are connected to the grid in sequence according to time and superimposed to finally output stable power.

[0023] More specifically, in step S1, the motion model and the power model are determined by the laws of physics: ; Among them, v is the falling velocity of the weight block, a is the acceleration during the fall, F is the tension of the rope on the weight, g is the acceleration due to gravity, t is time, M is the mass of the weight block, and P is power.

[0024] The efficiency model, as a constraint of the optimization model, the efficiency of the optimized system should not be lower than the set value. The loss comes from the air resistance and inner wall friction encountered by the weight during operation, and is related to the efficiency of power electronic equipment, mechanical equipment, and motor equipment: ; Where η is the system efficiency, H is the shaft height, and f s is the sliding friction, C w is the air resistance coefficient, A is the windward area, ρ is the air density, T is the atmospheric temperature, η motor is the gearbox efficiency, η inv is the converter efficiency, η G is the motor efficiency.

[0025] The economic model is the levelized cost per kilowatt-hour, which is an important indicator for measuring the economic benefits of energy storage systems. As a constraint of the optimization model, the optimized system levelized cost per kilowatt-hour should be less than the set value. The composition of the levelized cost per kilowatt-hour is: ; Among them, LCOE is the levelized cost of electricity, C total is the life cycle cost, life cycle cost C total Including initial investment cost C inv , Annual maintenance and operation cost C OM , replacement cost C R , Power purchase cost C C , Recovery cost C Rec , E life is the life cycle energy output value.

[0026] In step S2, the optimization objectives are power fluctuation rate and average output power: ; Among them, P max is the maximum power, Pmin is the minimum power value, is the average power. The optimization quantity is the motion parameters of the weight block, which include acceleration a, maximum velocity v m , acceleration change rate j.

[0027] In step S3, the multi-objective optimization algorithm is a genetic algorithm. The core of the genetic algorithm is a cycle - applying the genetic operators of selection, crossover and mutation in sequence, and then re-evaluating the individuals until the stopping condition is met, such as Figure 3 In the system optimization of this solution, it is specifically manifested as follows: 1. Generate N solutions that meet the constraints according to the set initial population number; 2. Calculate the fitness function of each individual, that is, optimize the target power fluctuation function f1(x) and the average output power function f2(x); 3. Apply genetic operators of selection, crossover, and mutation to the population to generate a new generation based on the better individuals in the current generation; 4. A new generation is continuously generated according to the set number of iterations K until the set number of iterations is reached or there is no obvious improvement in the individual, then the calculation is stopped.

[0028] The advantage of the present invention brought by the algorithm described in step S3 is that it is applicable to the situation where the motion parameters are complex in the shaft-type gravity energy storage system, and is applicable to the complex optimization problems with non-concave and nonlinear power fluctuation rate functions, and has good global search capabilities and a faster convergence speed.

[0029] In step S4, the power superposition method is specifically manifested in that multiple subsystems are staggered to start, and the output power of each subsystem is different in phase, and stable output power is obtained by peak shaving filling of power superposition. The grid connection time (i.e., staggered start time) of each subsystem is determined by the optimized motion parameters. Different motion parameters determine different vt curves, and then determine different Pt curves to obtain corresponding grid connection time.

[0030] The advantage of the method in the present invention is that the grid connection time does not need to be regulated and is determined by the optimized motion parameters: in order to obtain a stable output power, the superimposed output power must change periodically, so the grid connection time of each subsystem is: t(i) =i*T 下 / n, Where t(i) is the grid connection time of the ith subsystem, n is the total number of subsystems, T 下 is the single drop time of the weight block.

[0031] In summary, when the power demand of the power grid is determined, the above method can be used to achieve stable power output while meeting the power demand of the power grid; achieve maximum power output while meeting the power fluctuation rate demand of the power grid; and meet the grid connection demand of the power grid.

Claims

1. A multi-objective optimization method for stabilizing the output power of a vertical shaft gravity energy storage system, characterized in that: The steps include: Step S1, establishing a system motion model and a power model according to the physical process of the weight falling, and establishing an efficiency model and an economic model according to the selected system equipment; Step S2, using the motion model, efficiency model and economic model as constraints, and under the condition of meeting the power demand of the power grid, establishing a target optimization function with the goal of minimizing power fluctuation rate and maximizing output power, and the optimization quantity is the motion parameter of the heavy object; Step S3, setting the initial population size N and the number of iterations K, using a genetic algorithm to solve the optimization model, and obtaining the motion parameters of the heavy object; Step S4, determine the vt curve of the weight block and the grid connection time of multiple subsystems according to the motion parameters, and then obtain the output power Pt curve of a single subsystem in the system according to the vt curve. Multiple subsystems are connected to the grid in sequence according to time and superimposed to finally output stable power.

2. The multi-objective optimization method for stabilizing the output power of a vertical shaft gravity energy storage system according to claim 1, characterized in that: The motion model and power model are determined by the laws of physics: ； Among them, v is the falling velocity of the weight block, a is the acceleration during the fall, F is the tension of the rope on the weight, g is the acceleration due to gravity, t is time, M is the mass of the weight block, and P is power.

3. The multi-objective optimization method for stabilizing the output power of a vertical shaft gravity energy storage system according to claim 1, characterized in that: The efficiency model is a constraint condition of the optimization model. The efficiency of the optimized system should not be lower than the set value. The loss comes from the air resistance and inner wall friction during the operation of the weight, as well as the efficiency of power electronic equipment, mechanical equipment, and motor equipment: ； Where η is the system efficiency, H is the shaft height, and f s is the sliding friction, C w is the air resistance coefficient, A is the windward area, ρ is the air density, T is the atmospheric temperature, η motor is the gearbox efficiency, η inv is the converter efficiency, η G is the motor efficiency.

4. The multi-objective optimization method for stabilizing the output power of a vertical shaft gravity energy storage system according to claim 1, characterized in that: The economic model is the levelized cost of electricity, which is an important indicator for measuring the economic benefits of energy storage systems. As a constraint of the optimization model, the levelized cost of electricity of the optimized system should be less than the set value; composition: ； Among them, LCOE is the levelized cost of electricity, C total is the life cycle cost, life cycle cost C total Including initial investment cost C inv , Annual maintenance and operation cost C OM , replacement cost C R , Power purchase cost C C , Recovery cost C Rec , E life is the life cycle energy output value.

5. The multi-objective optimization method for stabilizing the output power of a vertical shaft gravity energy storage system according to claim 1, characterized in that: In step S2, the optimization objectives are power fluctuation rate and average output power: ； Among them, P max is the maximum power, P min is the minimum power value, is the average power; the motion parameters of the weight block include acceleration a, maximum velocity v m , acceleration change rate j.

6. The multi-objective optimization method for stabilizing the output power of a vertical shaft gravity energy storage system according to claim 1, characterized in that: In step S3, the genetic algorithm specifically includes: Generate N solutions that meet the restriction conditions according to the set initial population number; Calculate the fitness function of each individual, that is, optimize the target power fluctuation function and the average output power function; Apply the genetic operators of selection, crossover, and mutation to the population to produce a new generation based on the better individuals in the current generation; A new generation is continuously generated according to the set number of iterations K until the set number of iterations is reached or there is no obvious improvement in the individual, then the calculation is stopped.

7. The multi-objective optimization method for stabilizing the output power of a vertical shaft gravity energy storage system according to claim 1, characterized in that: In step S4, the power superposition method is specifically to start multiple subsystems at different times, and the output power of each subsystem has different phases, and a stable output power is obtained by peak shaving and filling by power superposition; the grid-connected access time of each subsystem is determined by the optimized motion parameters, and different motion parameters determine different vt curves, and then determine different Pt curves to obtain the corresponding grid-connected access time.

8. The multi-objective optimization method for stabilizing the output power of a vertical shaft gravity energy storage system according to claim 7, characterized in that: The grid connection time does not need to be regulated, but is determined by the optimized motion parameters; the output power after superposition changes periodically, and the grid connection time of each subsystem is: t(i) =i*T 下 / n, Where t(i) is the grid connection time of the ith subsystem, n is the total number of subsystems, T 下 is the single drop time of the weight block.

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